Abstract
Using a classical gravity model, this paper examines the effects of geopolitical risks on the trade flows, among 164 developing and developed countries, for the period of 1985–2013. For this purpose, we use the new index of geopolitical risks (GPR index). To the best of our knowledge, this is the first paper in the literature that considers the new GPR index in a gravity model. The paper implements the fixed-effects (FE), the random-effects (RE), the Hausman–Taylor (HT), and the Poisson Pseudo-maximum Likelihood estimations. The findings indicate that geopolitical risks negatively affect the trade flows. The paper also discusses the potential policy implications.
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Notes
While there are other indicators of geopolitical risks, the GPR indices created by Caldara et al. (2018) and Caldara and Iacoviello (2018) overcome various shortcomings of these indices that make them poorly suited for empirical analysis. First, many of the other indices either do not define geopolitical risk or use a wide-ranging definition that includes very different events, ranging from wars to the major economic crises to climate change. Naturally, it is unclear what these indices measure. Second, existing indices are extremely hard to replicate, with these indices, primarily constructed by private companies often not publicly available, being constructed subjectively, and come with a less-than-transparent methodology. Third, many of the indices exhibit very little variation and are available only for a few years. Also, many of them are qualitative indicators of whether countries are politically stable, and are reported using color-coded maps or integer numbers ranging from one to five.
The relation between conflict and international trade has been the focus of mainly political scientists who examine the impact of trade on the likelihood of conflict among countries and also the impact of conflict on international trade. The latter line of research among the scientists; see e.g., Anderton and Carter (2001), Barbieri and Levy (1999), Garfinkel et al. (2008, 2015), Keshk et al. (2004), Mansfield and Bronson (1997), Mansfield and Pevehouse (2000), Morrow et al. (1998, 1999), and Pollins (1989a, b).
Caldara and Iacoviello (2018) also examine the impact of global GPRs on capital inflows in 22 advanced economies, 23 emerging markets, and the U.S. Their results indicate that the GPR indices reduce capital inflows into the emerging markets, but causes an increase into the advanced economies.
Note that due to the limitation of the dataset, we use the GPR index of 18 countries in the empirical analysis. This makes our dataset as trade flows from 18 emerging countries to 164 countries.
See “Appendix 2” for the details of each group to construct the GPR index.
Note that 37%\(\cong (e^{0.32} - 1)\).
For details of the great trade collapse of 2008–2009, refer to Levchenko et al. (2010). There are also different hypotheses to explain the great trade collapse of 2008–2009. For their details, refer to Alessandria et al. (2010), Bems et al. (2011), Chor and Manova (2012), Eaton et al. (2016), and Novy and Taylor (2014).
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Appendices
Appendix 1: List of the countries in the dataset
Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, the Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belize, Benin, Bermuda, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo Democratic Republic, Costa Rica, Cote d`Ivoire, Croatia, Cuba, Cyprus, the Czech Republic, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Ethiopia, Fiji, Gabon, Gambia The, Georgia, Ghana, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hong Kong, Hungary, India, Indonesia, Iran, Iraq, Israel, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, Korea Republic, Kosovo, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Macao, Macedonia, Madagascar, Malawi, Malaysia, Maldives, Mali, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, New Zealand, Nicaragua, Niger, Nigeria, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, the Philippines, Poland, Qatar, Romania, Russia, Rwanda, Samoa, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovak Republic, Slovenia, Solomon Islands, Somalia, South Africa, Sri Lanka, St. Kitts & Nevis, St. Lucia, St. Vincent & Grenadines, Sudan, Suriname, Swaziland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad & Tobago, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, the United Arab Emirates, United Kingdom, United States, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, West Bank and Gaza, Yemen, Zambia, and Zimbabwe.
Appendix 2: Details of six groups in geopolitical risks (GPR) Index
Group 1 includes words associated with explicit mentions of geopolitical risk, as well as mentions of military-related tensions involving large regions of the world and the U.S. involvement. Group 2 includes words directly related to nuclear tensions. Groups 3 and 4 include mentions related to the war threats and terrorist threats, respectively. Finally, Groups 5 and 6 aim at capturing press coverage of actual adverse geopolitical events (as opposed to just risks) which can be reasonably expected to lead to increases in geopolitical uncertainty, such as terrorist acts or the beginning of a war.
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Gupta, R., Gozgor, G., Kaya, H. et al. Effects of geopolitical risks on trade flows: evidence from the gravity model. Eurasian Econ Rev 9, 515–530 (2019). https://doi.org/10.1007/s40822-018-0118-0
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DOI: https://doi.org/10.1007/s40822-018-0118-0
Keywords
- Geopolitical risks
- Trade flows
- International trade
- Gravity model
- Emerging economies
- Panel data estimation techniques